class_docstr: Abstract object detector dataset module.
  
  This module implements some of the basic functionalities every object
  detector dataset usually needs.
  
  Object detection datasets are datasets that have ground-truth information
  consisting of rectangular bounding boxes.
  
  Attributes:
      dataset_dir (str): Base directory of the dataset.
      num_epochs (int): Number of epochs the dataset should iterate over.
      batch_size (int): Batch size the module should return.
      split (str): Split to consume the data from (usually "train", "val" or
          "test").
      image_min_size (int): Image minimum size, used for resizing images if
          needed.
      image_max_size (int): Image maximum size.
      random_shuffle (bool): To consume the dataset using random shuffle or
          to just use a regular FIFO queue.
docstr: Save general purpose attributes for Dataset module.
  
  Args:
      config: Config object with all the session properties.
docstr_tok: ["Save","general","purpose","attributes","for","Dataset","module",".","Args",":","config",":","Config","object","with","all","the","session","properties","."]
code: def __init__(self, config, name="object_detection_dataset", **kwargs):
      """
      Save general purpose attributes for Dataset module.
  
      Args:
          config: Config object with all the session properties.
      """
      super(ObjectDetectionDataset, self).__init__(config, **kwargs)
      self._image_min_size = config.dataset.image_preprocessing.get(
          "min_size"
      )
      self._image_max_size = config.dataset.image_preprocessing.get(
          "max_size"
      )
      # In case no keys are defined, default to empty list.
      self._data_augmentation = config.dataset.data_augmentation or []
  
file_path: luminoth/luminoth/datasets/object_detection_dataset.py
imports: import tensorflow
  from luminoth.datasets.base_dataset import BaseDataset
  from luminoth.utils.image import resize_image_fixed, resize_image, flip_image, random_patch, random_resize, random_distortion, expand
label_desc: Use image node_id 1 to create a new ObjectDetectionDataset object from inherited base classes: BaseDataset with example: obj = ObjectDetectionDataset(config, name)